A contrastive learning transformer embeds network flow sequences to enable correlation clustering that groups scanner sources consistently with labels.
Netgpt: Generative pretrained transformer for network traffic
8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
Protocol-aware tokenization is the primary performance driver for wireless packet foundation models, delivering 32-point accuracy gains while architecture changes yield only 2 points in a controlled 2x2 comparison.
A compact Mamba-2 model performs end-to-end byte-level network traffic classification without tokenization or pre-training and remains competitive with substantially larger pre-trained systems.
Nepco offloads network foundation models to SmartNICs using localized byte-sequence modeling and a pattern-aware convolutional architecture to achieve competitive macro F1 scores with 328x lower end-to-end latency than prior foundation models.
NetVAD uses a strictly identifier-free VAE on frozen foundation model embeddings, trained solely on benign traffic, to achieve 98% micro F1 and 96% macro F1 on ToN-IoT for unsupervised intrusion detection.
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
In-domain BERT pretraining yields better low-FPR detection of DNS exfiltration than random initialization, with larger gains when more labeled fine-tuning data is available.
A survey reviewing statistical and deep learning approaches to synthetic network traffic generation, with comparisons, an AI comparison tool, open challenges, and future directions.
citing papers explorer
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Contrastive Learning and Correlation Clustering for Sequences of Network Telescope Data
A contrastive learning transformer embeds network flow sequences to enable correlation clustering that groups scanner sources consistently with labels.
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Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models
Protocol-aware tokenization is the primary performance driver for wireless packet foundation models, delivering 32-point accuracy gains while architecture changes yield only 2 points in a controlled 2x2 comparison.
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MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining
A compact Mamba-2 model performs end-to-end byte-level network traffic classification without tokenization or pre-training and remains competitive with substantially larger pre-trained systems.
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Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNIC
Nepco offloads network foundation models to SmartNICs using localized byte-sequence modeling and a pattern-aware convolutional architecture to achieve competitive macro F1 scores with 328x lower end-to-end latency than prior foundation models.
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NetVAD: Foundation-Model Representation Learning for Identifier-Free Unsupervised Intrusion Detection
NetVAD uses a strictly identifier-free VAE on frozen foundation model embeddings, trained solely on benign traffic, to achieve 98% micro F1 and 96% macro F1 on ToN-IoT for unsupervised intrusion detection.
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UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
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Improving DNS Exfiltration Detection via Transformer Pretraining
In-domain BERT pretraining yields better low-FPR detection of DNS exfiltration than random initialization, with larger gains when more labeled fine-tuning data is available.
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A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
A survey reviewing statistical and deep learning approaches to synthetic network traffic generation, with comparisons, an AI comparison tool, open challenges, and future directions.